• 한국도서관정보학회지
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• 제34권4호
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• pp.179-202
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• 2003

독서는 가장 전형적인 지식활동, 정보활동이다. 독서는 현대인들에게 있어서 생활의 일부이며 그 자체이다. 이러한 생활의 일부로서 독서활동은 새로운 정신세계를 창출하는 인지적이며 사회적인 활동이다. 그러나 우리나라 교육현장에서 많은 독서교육의 문제점이 나타나고 있다. 이러한 문제를 해결하기 위해서는 독서능력 신장을 위한 독서활동방안과 학습능력 신장을 위한 독서활동방안이 체계적으로 이루어져야 한다. 이를 위해 이 연구에서는 독서활동 활성화를 위한 방안으로서 기존의 독서학습활동의 문제점을 분석하고 이를 기반으로 새로운 독서학습활동을 탐색하여 활성화에 대한 모형을 제안하고자 한다. 이것은 독서학습 모형을 기반으로 한 지식창조형 독서활동 모형으로서 독서콘텐츠 관리이다.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.241-249
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• 2021

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.83-92
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• 2023

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

• 한국비블리아학회지
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• 제29권3호
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• pp.209-228
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• 2018

본 연구는 고등학교 "국어" 교과서의 한 학기 한 권 읽기 학습활동을 분석하여 학교도서관과 협력 방안을 모색하는 것이다. 그동안 독서 활동은 교과 지식과 이원화되어 정규 교과 수업 시간 밖에서 이뤄지는 경우가 많았다. 한 학기 한 권 읽기는 "국어" 정규 수업 시간 내 이뤄지는 독서 활동으로, <읽기-생각나누기-표현하기>를 기본 모형으로 개발되었다. 하지만 실재 11종 "국어" 교과서 22개 유형의 한 권 읽기 학습활동을 분석한 결과, 한 권 읽기는 <도서 정하기-읽기-표현하기>로 실현되었다. 단계별 학교도서관과 협력방안으로, 도서 정하기 단계는 학교도서관을 대상 도서 검색 공간으로 활용하고, 읽기 단계는 독서전략을 추가, 보완해 독서일지를 재구성하며, 표현하기 단계는 기존 독후 프로그램을 함께 운영할 수 있다. 이와 같이 한 권 읽기의 단계별로 협력함으로써 학교도서관의 교육적 역할은 더욱 견고해질 수 있으리라 기대된다.

• ETRI Journal
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• 제43권5호
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

• 영어어문교육
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• 제11권2호
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• pp.19-34
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• 2005

Hypertext raises question to general assumptions about our conventional conceptions of education. In this essay, three kinds of learning-models are presented by the application of "writing" hypertext literature to the English education of the elementary school. These models, which I call the "scene-centered" system, give knowledge to learners in non-linear, non-sequential structure. The term "scene" is a single concept or idea composed of a single sub-text, which is to be made by the group of students. This system is focused on the collaborative composition of students. Students, by generating sub-texts and connecting texts, perform the educational activities to expand the source text. The "scene-centered" system is, to put it into a Barte's term, a "writerly text." But in order to "write," "reading" should be accompanied. So, this system is a learning model in which writing and reading are carried on simultaneously. In all the process, students play a role of multi-user, with three access rights: read, write, and annotate. So, students making use of hypertext systems will act as reader-authors. And teachers will take the new role in collaborative writing environment. No longer the central authoritarian evaluator, they will become consultants, co-writers, coaches of their students.

• Journal of information and communication convergence engineering
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• 제19권1호
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• pp.48-53
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• 2021

Herein, we propose a document analysis system that analyzes papers or reports transformed into XML(Extensible Markup Language) format. It reads the document specified by the user, extracts keywords from the document, and compares the frequency of keywords to extract the top-three keywords. It maintains the order of the paragraphs containing the keywords and removes duplicated paragraphs. The frequency of the top-three keywords in the extracted paragraphs is re-verified, and the paragraphs are partitioned into 10 sections. Subsequently, the importance of the relevant areas is calculated and compared. By notifying the user of areas with the highest frequency and areas with higher importance than the average frequency, the user can read only the main content without reading all the contents. In addition, the number of paragraphs extracted through the deep learning model and the number of paragraphs in a section of high importance are predicted.

• 한국멀티미디어학회논문지
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• 제16권1호
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• pp.100-107
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• 2013

최근 전자책 시장이 활성화됨에 따라 전자책 단말기에서부터 스마트 기기의 소프트웨어 리더까지 각종 전자책뷰어가 등장하고 있다. 하지만 시각장애인, 난독증, 학습장애인과 같은 독서장애인을 위한 모바일 전자책 인터페이스에 대한 개발과 연구는 부족한 실정이다. 비장애인을 대상으로 만들어진 전자책뷰어는 독서장애인에게 그대로 적용할 수 없기 때문에 독서장애 사용자의 특성에 따라 차별화된 인터페이스가 요구된다. 이에 본 논문에서는 독서장애인용 전자책 표준 형식을 지원하는 모바일 전자책 뷰어 인터페이스 모델을 제안한다. 제시 모델은 전맹인, 저시력인, 학습장애인 등 사용자의 특성 및 상황(context)에 따라 차별화된 인터페이스를 제공한다. 아울러 독서장애인용 어노테이션 시스템을 지원함으로써 기존의 독서장애인용 오디오북과는 다른 사용자-전자책 간의 상호작용을 지원한다. 또한 본 모델을 이용하여 스마트폰 플랫폼인 안드로이드 환경에서의 독서장애인용 전자책뷰어 프로토타입을 구현하고 그 활용 가능성을 제시한다. 본 연구의 결과는 국내 인구 10%에 해당하는 독서장애인의 효율적인 독서활동을 지원할 수 있다.

• Journal of information and communication convergence engineering
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• 제16권3호
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• pp.160-165
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• 2018

This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.

• 중국학논총
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• 제61호
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• pp.255-277
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• 2019

This paper presented a course plan based on the ideas I gained from conducting a lecture on Chinese language for students in the second semester of the Chinese language department at a four-year university. In the paper, we sought to deviate from the traditional grammar-translation teaching style and find ways for students to enjoy learning without difficulty in all areas by using the 'total language approach' such as writing, speaking, listening and reading through reading skills. Therefore, we discussed the educational significance and expression of the 'Chinese Languages' class, and introduced the class stages and methods of progress. In other words, they suggested introduction of text plots, explanation of vocabulary and grammar, presentation of original text, questions about text, arrangement of words, ordering sentences to fit the plot, and understanding the plot while looking at the picture.